Using different scheduling algorithms can affect the performance of mobile
cloud computing using Hadoop MapReduce framework. In Hadoop MapReduce framework,
the default scheduling algorithm is First-In-First-Out (FIFO). However, the FIFO
scheduler simply schedules task according to its arrival time and does not consider any
other factors that may have great impact on system performance. As a result, FIFO cannot
achieve good performance in Hadoop for mobile cloud computing. In this paper, we
propose a novel scheduling algorithm, called FSLA (FIFO with Shareability and Locality
Aware). FSLA is a FIFO-based scheduling policy that considers locality of required data
and data sharing probability between tasks. The tasks requesting the same data can be
gathered, easily batch processed, and thus reduce the overhead of transferring data between
data nodes and computations nodes. The simulation results show that compared
to FIFO, FSLA can reach 65% improvement in system performance.